Edge AI/ML Engineer for Embedded Systems

HP Inc.; Spring (TX); On-site; USD 147,000 - 231,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Disability insurance

Employee assistance program

Flexible spending account

Life insurance

Parental leave

Paid holidays

Job summary

HP, Inc. is seeking an Embedded AI/ML Developer to design, develop, and optimize AI-enabled embedded software for HP’s commercial PC and connected device portfolio. The role focuses on deploying efficient models at the edge and integrating AI with firmware and system software.

The engineer will collaborate with hardware, firmware, software, and data science teams to translate AI concepts into production-ready implementations, performance-tuned and power-aware on constrained hardware.

Qualifications

• Bachelor's or Master's in computer science, computer engineering, statistics, mathematics, AI, ML, robotics or related field.
• 7–10 years of experience in embedded software, firmware, AI/ML deployment, edge inference, or system-level software development.
• Experience with edge AI deployment and hardware integration.

Responsibilities

• Designs, develops, and optimizes embedded AI/ML software for edge devices in HP's commercial PC and connected device portfolio.
• Converts AI/ML algorithms into production-quality embedded implementations optimized for latency, memory, power, and compute constraints.
• Integrates machine learning inference engines and accelerators into embedded firmware and system software environments.

Skills

C/C++

Python

Embedded debugging

Edge AI

Education

Bachelor’s or Master’s degree in CS/CE/AI or related

Tools

TensorRT

ONNX

TensorFlow Lite

OpenVINO

PyTorch

TensorFlow

Job description

HP, Inc. is seeking an Embedded AI/ML Developer to design, develop, and optimize AI-enabled embedded software for HP’s commercial PC and connected device portfolio. The role focuses on deploying efficient models at the edge and integrating AI with firmware and system software.

The engineer will collaborate with hardware, firmware, software, and data science teams to translate AI concepts into production-ready implementations, performance-tuned and power-aware on constrained hardware.